The goal of our work was to maximize gas production and recovery from a horizontal appraisal well in the Mancos shale in New Mexico. This required a fracture design that would maximize perforation cluster efficiency and a lateral placement strategy that would maximize gas recovery. A key challenge was to design a fracture treatment that would overcome the extreme stress shadowing effects. Another key challenge was to optimize the lateral placement balancing multiple factors.
Fracture treatment simulations were completed for various designs. Fracture simulations indicated cluster efficiency could be dramatically improved by optimizing the way we pump the pad. A step-up technique for increasing pumping rates during the pad stage helped to initiate more fractures. Intra-stage diversion was utilized. Fracture simulations were performed to optimize the lateral placement. This required balancing multiple factors to access the highest gas-in-place (GIP) interval yet facilitate more fracture initiations per stage.
Fracture descriptions from the fracture simulations were input to a reservoir simulator to determine the optimal design. This paper will focus on the hydraulic fracture modeling.
This appraisal well was the most productive Mancos gas well ever delivered in the San Juan Basin. The 9,546’ lateral produced at a choke constrained plateau rate of about 13 MMscfd for 7 months and produced over 6 BCF in the first 20 months. A radioactive tracer log indicated an overall perforation cluster efficiency of 83%, a significant achievement in a shale with high stress shadowing.
The fracturing fluid design, diverter design and pumping techniques can be applied in many other shales as a low-cost way to increase perforation cluster efficiency, which will in turn result in higher production rates and higher cumulative recovery. Building on the success observed in the Mancos wells, BP and BPX Energy have subsequently utilized these techniques in other shale plays with success.
The concepts and workflow used to decide the optimal lateral placement is a well-defined approach that can be applied to other unconventional wells to increase hydrocarbon recovery.
Travers, Patrick (Dolan Integration Group) | Burke, Ben (HighPoint Resources) | Rowe, Aryn (HighPoint Resources) | Hodgetts, Stephen (Dolan Integration Group) | Dolan, Michael (Dolan Integration Group)
Scope: The management, treatment and disposal of hydraulic fracturing flowback fluids and produced water presents a major challenge to operators. Though the volumes of water are tracked closely during operations, the sources of that water are not well understood. The objective of this study is to apply a cost effective and proven technique, stable isotope analysis, along with an extensive sampling program (n>1,500 samples) to describe the contributions of variable water sources through completions, flowback and the production lifecycle of multiple horizontal, hydraulically fractured wells in the Denver Basin, Colorado.
Methods: The water stable isotopes of hydrogen (1H and 2H) and oxygen (16O and 18O) are conservative tracers and particularly advantageous because they occur naturally in these systems and rely on well-established scientific and analytical techniques. Sample collection is simple and does not require specialized equipment or operational downtime. 80 horizontal, hydraulically fractured wells completed in the Cretaceous Niobrara or Codell Formations were selected for this study. More than 1,500 samples were collected and analyzed in total, including: baseline samples of the source water used to stimulate the well, time series samples collected at daily or semi-daily intervals during the early weeks of flowback, and samples collected several months after the wells were brought on production. Samples of produced water were also collected from legacy wells in the field as well as offset wells being monitored for frac hits during completions.
Results: Samples of the near surface and shallow aquifer source water collected prior to hydraulic fracturing fell on or near the global meteoric water line (GMWL) as defined by Craig (1961). This isotopic signature is expected for modern water in aquifers charged by precipitation. In contrast, samples collected during flowback and production were significantly enriched in 2H and 18O. Furthermore, the magnitude of the isotopic difference between the source and flowback water increased with time until equilibrating after several months. This equilibrated composition is consistent for Niobrara and Codell wells in the field, as well as legacy wells sampled and consequently is hypothesized to be indicative of native formation water. The study did find exceptions, particularly with wells known to be connected to major fault or fracture networks. These samples deviated from typical formation water signatures, potentially indicating the migration of deeper sourced fluids or the vertical mixing of shallower fluids with Cretaceous waters.
Significance: The scale of this study is unique in the literature and provides novel and comprehensive insight into the dynamics of flowback and the sources of produced water in the Denver Basin. This study demonstrates that these data can clearly differentiate water injected during stimulation from native formation waters, as well as track the magnitude and duration of well cleanup. It can also identify wells that may be producing water with a unique composition due to fluid migration through faults or fracture networks or due to nearby well communication.
The Niobrara interval in the Denver-Julesberg (DJ) Basin contains several important unconventional hydrocarbon targets. However, the Niobrara is extensively faulted, which poses challenges for accurately landing and steering laterals in zone. Insight into small faulted structures in the Niobrara using traditional manual fault interpretation techniques is challenging because of the tuning thickness in seismic data. Fault throws less than the tuning thickness are difficult to interpret and incorporate into geosteering plans. Consequently, drillers frequently find themselves out of zone after crossing these small faults. Using independent information about fault locations and throws provided from multiple horizontal wells in the DJ Basin, this paper demonstrates the fault likelihood attribute (Hale, 2013) can resolve fault throws as small as 10 ft, allowing seismic-based well plans and unconventional project economics to be significantly improved.
Traditional geoscience data interpretation workflows in support of well planning can be tedious and time consuming, requiring manual fault picking on seismic profiles in conjunction with horizon tracing and gridding for structural mapping. The emergence of unconventional resource plays requires both more efficient geoscience workflows to support round-the-clock drilling operations and more detailed structural interpretations to help ensure laterals are steered along sweet spots. Pre-drill mapping of small-scale faults is therefore of particular importance for safe operations and helping ensure that lateral wells stay in zone.
Recent advances in fault-sensitive post-stack seismic attributes are changing the way subsurface professionals think about faults and how to map them in 3D space. In particular, the fault likelihood attribute (Hale, 2013) has provided a breakthrough improvement in the quality of seismic-derived fault attributes. Typically, the fault likelihood attribute is used in exploration settings to rapidly generate a broad-scale structural interpretation, being used both as a guide to manual fault interpretation and as input into automated fault extraction algorithms. This paper demonstrates the value of fault likelihood in development settings for assisting the well planning and geosteering process.
Dreyer, Daniel (Nalco Champion, An Ecolab Company) | Kurian, Pious (Nalco Champion, An Ecolab Company) | Hu, Thomas (Nalco Champion, An Ecolab Company) | Tonmukayakul, Peng (Nalco Champion, An Ecolab Company) | Calaway, Ronald (Quintana Energy Services) | Hodges, Clint (Quintana Energy Services) | Peoples, Kevin (Quintana Energy Services)
The use of degradable polymeric materials to control fluid flow during hydraulic fracturing (referred to as “diversion”) is an increasing area of interest in well completions. While poly(lactic acid) (PLA) and other similar polyesters dominate the market space, there are drawbacks to these materials that can limit their performance. Specifically, if the particle size distribution is not matched to the geometries of the perforations and fractures, it will be difficult or impossible to achieve optimal plugging/jamming, and the fluid will not be efficiently diverted into un- or under-stimulated portions of the formation. We have developed an alternative approach to fluid diversion that retains the key properties of polymeric diverters, including product degradability and an ability to withstand high hydraulic pressure, while allowing for better sealing efficiency with less sensitivity to the precise particle size distribution. In this paper, we describe a product that is intended for use in lower- to mid-temperature applications (approximately 160-200 °F). Our laboratory and field results show this product can both seal efficiently and adaptably, while also withstanding high hydraulic pressure.
During completion operations, it is typical that only a fraction of the perforations generated will accept stimulation fluid, and then contribute to production once a well is brought online. Estimates vary, but by at least one account, as few as 50% of the perforation clusters are effectively simulated (Miller 2011), leaving significant portions of the formation un- or under-stimulated. As a result, implementation of strategies for the diversion of fracturing fluid during well completions has become increasingly common in field operations (Van Domelen 2017), and one of the more common methods focuses on the application of particulate chemical treatments (Weddle 2017). Commonly referred to as “diverters,” these treatments are typically comprised of blends of controllably, but variably, sized solids that temporarily plug high permeability perforations and/or fractures (Trumble 2019). When these plugs form, the fluid is then redirected into the un- or under-stimulated portions of the reservoir (Allison 2011, Astafyev 2016, Fry 2016, Rahim 2017), ultimately leading to improved production.
Jin, Xiaochun (Jacob) (ULTRecovery Corporation) | Pavia, Michael (ULTRecovery Corporation) | Samuel, Michael (ULTRecovery Corporation) | Shah, Subhash (University of Oklahoma, Norman) | Zhang, Rixing (ULTRecovery Corporation) | Thompson, James (ULTRecovery Corporation)
Historical production data of unconventional oil wells shows rapid decline rate and low estimated ultimate recovery (EUR), although the records of “lateral length” and “number of stages” have been broken frequently in Permian Basin. The industry has been striving to develop a novel technically feasible and economic enhanced oil recovery (EOR) technology to arrest the production decline curve; however, limited successes have been achieved.
According to the dialectical analysis of the four-dimensional dynamic interactions between unconventional rock-slickwater system-subsurface water-indigenous beneficial bacteria, it is concluded that the rapid decline rate and low EUR might be attributed to the potential formation damage caused by (1) the adsorption of high-weight big organic molecules (gellants and HPAM) on nanopores, (2) plugging of natural fractures, (3) plugging of propped fractures, and (4) pressure and energy loss while liquid flowing through the polluted zones. An advanced biotechnology is developed to unblock the contaminated zones by injecting microbial nutrients to the stimulated reservoir volume (SRV) to grow the indigenous beneficial microbes to degrade the residual fracturing fluid chemicals. The otherwise blocked flow paths are re-opened, and the trapped fluids (oil, gas, and water) can be mobilized, the residual oil can flow from the reservoir to the borehole with less pressure loss. Therefore, the objective of the field pilots of unconventional EOR is to create a more permeable SRV.
A ULTRSHALE™ process for unconventional EOR is developed and has been proven to be effective based on the laboratory study and field tests. One depleted fractured vertical well (used crosslinked guar-based fracturing fluid, at about 9,000 ft) and one depleting fractured horizontal well (used slickwater system, at about 9,900 ft) were selected as the field pilots of unconventional EOR in the Permian Basin. The laboratory data indicated that the indigenous beneficial microbes residing in the deep reservoir could be stimulated to degrade the fracturing fluid additives in the high-salinity produced water at an elevated temperature. The field implementation was carried by a Huff-N-Puff process. The post-treatment liquid production was uplifted by 40%-127% within 180 days, which means the otherwise polluted SRV was unblocked by the stimulated beneficial microbes. Furthermore, the eight-months incremental oil of the vertical well was about 1,500 bbls, the six-months incremental oil of the fractured horizontal well was about 12,000 bbls. The incremental of EUR of the fractured vertical and horizontal wells were 2,100 bbls and 25,000 bbls, respectively. And the EUR after the treatment is increased by 9-12%. The payouts for both treatments were from 2-4 months. The Rate of Return (ROR) for both pilots is more than 100%.
Siddiqui, Fahd (University of Houston) | Rezaei, Ali (University of Houston) | Dindoruk, Birol (University of Houston / Shell International Exploration and Production) | Soliman, Mohamed Y. (University of Houston)
Prior knowledge of reservoir fluid type and properties aids in selecting and optimizing completion and surface facilities. Fluid properties prediction has an impact on in-place volumes and reservoir performance management including optimized well placement. We present a data-driven fluid variation modeling approach using machine learning. The aim is to predict the fluid type and oil API gravity for a given location and depth and optimize the completion design for the Eagle Ford shale.
Data from 9400 Eagle Ford shale wells were compiled, cleaned, and analyzed. Data was then divided into training and test sets. The test set was set aside for validation to prevent any training bias. Data visualization and statistical analysis was carried out, which revealed patterns and features within the training data. Three separate artificial neural networks (ANNs) were then constructed on those features, and a supervised learning algorithm was employed to train on the training set.
The first ANN predicts the oil API gravity based on a given coordinate: latitude, longitude and depth information. This network uses Mean Squared Error (MSE) loss function with the Root Mean Squared (RMS) regression optimizer. ANN-1 reported an error of 2.4 API which is well within process dependency of the API measurements and within the potential experimental errors. The second ANN predicts the most likely fluid type along with the probability, which can be used as a measure of confidence. ANN-2 uses the categorical cross-entropy loss function with the Adam optimizer (Kingma (2014)). Finally, ANN-3 predicts the hydrocarbon production of the first 12 months based on the well location, lateral length, depth, number of stages, proppant volume and gel volume. All three models were then validated on the test set, and a good match was obtained. Based on the data-driven models, an optimization scheme was created to maximize cash flow from the first 12 months of production based on varying the lateral length, the number of stages, proppant volume, and gel volume used. The resulting optimum parameters are then represented visually on the map of Eagle Ford, along with oil and gas production, and cash flow.
Even though the presented method was trained for Eagle Ford, data from other formations can be incorporated and re-trained, including other proxies for every additional basin, to create a general neural network predictive model on all formations; or to create smaller networks that would make accurate predictions within the specified formation. This approach will lead to a continuously improving and learning process for each additional field and play.
In self-sourced low-permeability reservoirs the efficiency at the interaction between the mudstone matrix and fractures is a key control on well performance. Commonly, the more heterogeneous (interbedded) the reservoir the more complex fracture network is naturally developed or can be achieved during stimulation. In this study, using observations from two different unconventional shale units, we demonstrate that mudstone stratigraphic heterogeneities are scale dependent, and thus capturing their expression at different scales is key to understanding the level to which facies arrangements can affect important petrophysical, geochemical and geomechanical properties. Characteristics from the Duvernay Formation in Alberta-Canada and the Woodford Shale in Oklahoma-USA were compared in this study; both units are Late Devonian in age and are organic-rich prolific reservoirs. Lithologies in the Duvernay mostly vary according to changes in carbonate content, whereas in the Woodford changes are according to quartz content. However, in both cases a systematic alternation of two distinct rock types is evident at the cm-scale in outcrops and cores: organic-rich and calcite-rich facies for the Duvernay, and mudstones and chert facies for the Woodford. By combining high-resolution geochemical and geomechanical data, two distinct trends were evident for both units, and illustrate that variations in organic contents, mineralogy and relative hardness can be grouped by the two main rock types for each unit. In the Duvernay, the calcite-rich facies occur as low-TOC beds, at the microscale these are dominated by pore-filling calcite cements. In the Woodford, chert beds present the lower TOC content and their microfabric consists of microcrystalline aggregates of biogenic/authigenic quartz. In both units, the higher porosity values correlate with the high-TOC beds with abundant interparticle porosity. As for mechanical hardness and natural fractures, the higher calcite and quartz contents positively correlate with stiffer beds which generally are more brittle and have more natural fractures. The interbedded character between high-TOC and low-TOC beds is common for both units but at different frequencies and thickness. Capturing the degree of interbedding using a heterogeneity index suggests that reservoir behavior might be depicted as a multi-layered model in which properties are affected by the thickness, permeability, storage capacity, stiffness and fracture frequency of each bed. Although sometimes neglected, the study of fine-scale variations in reservoir properties can provide significant criteria for the selection of optimal horizontal landing zones.
Seismic attributes can be both powerful and challenging to incorporate into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018, Geophysical Insights conducted a proof of concept on 100 square miles of multi-client 3D data jointly owned by Geophysical Pursuit, Inc. (GPI) and Fairfield Geotechnologies (FFG) in the Denver-Julesburg Basin (DJ). The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin.
The seismic data are from Phase 5 of the GPI/Fairfield Niobrara program in northern Colorado. A preliminary workflow which included synthetics, horizon picking and correlation of 28 wells was completed. The seismic volume was re-sampled from 2 ms to 1 ms. Detailed well time-depth charts were created for the Top Niobrara, Niobrara A, B and C benches, Fort Hays and Codell intervals. The interpretations, along with the seismic volume, were loaded into the Paradise® machine learning application, and two suites of attributes were generated, instantaneous and geometric. The first step in the machine learning workflow is Principal Component Analysis (PCA). PCA is a method of identifying attributes that have the greatest contribution to the data and that quantifies the relative contribution of each. PCA aids in the selection of which attributes are appropriate to use in a Self-Organizing Map (SOM). In this case, 15 instantaneous attribute volumes, plus the parent amplitude volume, were used in the PCA and eight were selected to use in SOMs. The SOM is a neural network-based machine learning process that is applied to multiple attribute volumes simultaneously. The SOM produces a non-linear classification of the data in a designated time or depth window.
For this study, a 60-ms interval that encompasses the Niobrara and Codell formations was evaluated using several SOM topologies. One of the main drilling targets, the B chalk, is approximately 30 feet thick; making horizontal well planning and execution a challenge for operators. An 8 X 8 SOM applied to 1 ms seismic data improves the stratigraphic resolution of the B bench. The neuron classification also images small but significant structural variations within the chalk bench. These variations correlate visually with the geometric curvature attributes. This improved resolution allows for precise well planning for horizontals within the bench. The 25 foot thick C bench and the 17 to 25 foot thick Codell are also seismically resolved via SOM analysis. Petrophysical analyses from wireline logs run in seven wells within the survey by Digital Formation; together with additional results from SOMs show the capability to differentiate a high TOC upper unit within the A marl which presents an additional exploration target. Utilizing 2d color maps and geobodies extracted from the SOMs combined with petrophysical results allows calculation of reserves for the individual reservoir units as well as the recently identified high TOC target within the A marl.
The results show that a multi-attribute machine learning workflow improves the seismic resolution within the Niobrara reservoirs of the DJ Basin and results can be utilized in both exploration and development.
Production from organic-rich shale petroleum systems is extremely challenging due to the complex rock and flow characteristics. An accurate characterization of shale reservoir rock properties would positively impact hydrocarbon exploration and production planning. We integrate large-scale geologic components with small-scale petrophysical rock properties to categorize distinct rock types in low porosity and low permeability shales. We then use this workflow to distinguish three rock types in the reservoir interval of the Niobrara shale in the Denver Basin of the United States: The Upper Chalks (A, B, and C Chalk), the Marls (A, B, and C Marl), and the Lower Chalks (D Chalk and Fort Hays Limestone). In our study area, we find that the Upper Chalk has better reservoir-rock quality, moderate source-rock potential, stiffer rocks, and a higher fraction of compliant micro- and nanopores. On the other hand, the Marls have moderate reservoir-rock quality, and a higher source rock potential. Both the Upper Chalks and the Marls should have major economic potentials. The Lower Chalk has higher porosity and a higher fraction of micro-and nanopores; however, it exhibits poor source rock potential. The measured core data indicates large mineralogy, organic-richness, and porosity heterogeneities throughout the Niobrara interval at all scale.
Unconventional petroleum systems are highly complex hydrocarbon resource plays both at the reservoir scale and at the pore scale (Aplin and Macquaker, 2011; Loucks et al., 2012; Hart et al., 2013; Hackley and Cardott, 2016). These organic-rich sedimentary plays, generally described as shale reservoirs, are composed of very fine silt-and clay-sized particles with grain sizes < 62.5 μm (Loucks et al., 2009; Nichols, 2009; Passey et al., 2010; Kuila et al., 2014; Saidian et al., 2014). They undergo extensive post-depositional diagenesis that transforms rock composition and texture, hydrocarbon storage and productivity, and reservoir flow features (Rushing et al., 2008; McCarthy et al., 2011; Jarvie, 2012; Milliken et al., 2012). Although some shale rock facies can retain depositional attributes during diagenesis, many critical reservoir properties, such as, mineralogy, pore structure, organic richness and present-day organic potential, etc., are significantly perturbed (Hackley and Cardott, 2016).
The main goal for an operator developing an unconventional reservoir project is to maximize NPV per acre by optimizing its completion strategy. This can be achieved by applying a comprehensive approach that accounts for key well treatment controlling parameters, their impact on the future production performance, and economic uncertainty. In this work, we developed and applied a workflow to explore the impact of various completion parameters and determine the completion strategy with the maximum economic gain.
The workflow integrates petrophysical well log and core data, along with PVT lab experiments with normalized permeabilities calculated from microseismic attributes to initialize the reservoir model. The reservoir model is then calibrated using actual field data to generate a history matched model. Since this model is developed based on microseismic data and represents a realistic network of fractures created during stimulation, it can be further used to analyze the impact of main completion parameters, well spacing and configuration, on the production performance of the wells.
The workflow is applied to three wells drilled in a gas reservoir in the Marcellus Shale. Because abundant field data were available, we can be certain that the calibrated reservoir model accurately matches the reservoir behavior. Detailed analysis of the reservoir model shows the presence of undepleted zones which indicates the current well spacing is too wide. However, the frac hits recorded through microseismic monitoring and pressure interference with nearby wells suggests a tighter well spacing will result in energy loss and over-stimulation. Therefore, an economic analysis is used to evaluate the various well spacing and configuration scenarios and their implications in terms of cost-benefits.
Various well spacing scenarios are created for the original and the proposed chevron pattern well configurations. For each scenario, the EUR, NPV per well, and NPV per acre are calculated to represent maximum gas production, the overall profitability of the pad, and the economic success of the project, respectively. Three gas price scenarios are used for calculation of the NPV's to analyze the impact of the market condition on the economics of the project. The analysis demonstrates that tighter well spacing, independent of gas price, leads to the improved NPV per acre, reduction of EUR, and an increase in well communication as shown by the newly developed well communication index. The models reveal that a monotonic relation between well spacing and NPV per acre does not exist due to the complex nature of the created fracture network and competition between two opposite factors: frac hits that arises at tighter well spacing and unstimulated zones that diminish.
We showed that obtaining optimized well spacing and configuration could only be achieved through applying a comprehensive workflow that not only accounts for the impact of various well design and configuration parameters on production but also their economic implications defined in terms of NPV per acre. It is important to note that the integration of microseismic data was essential for the success of the workflow since it provides a realistic picture of the pathways connecting the adjacent wells which facilitate well communication.